AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network.
Computers in biology and medicine
Jan 22, 2025
Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some drawbacks, such as limited interpretability, extensive computational r...
Utilizing deep learning for automatic segmentation of the cochleae in temporal bone computed tomography.
Acta radiologica (Stockholm, Sweden : 1987)
Jan 22, 2025
BackgroundSegmentation of the cochlea in temporal bone computed tomography (CT) is the basis for image-guided otologic surgery. Manual segmentation is time-consuming and laborious.PurposeTo assess the utility of deep learning analysis in automatic se...
Robot-Assisted CT-Guided Biopsy with an Artificial Intelligence-Based Needle-Path Generator: An Experimental Evaluation Using a Phantom Model.
Journal of vascular and interventional radiology : JVIR
Jan 21, 2025
PURPOSE: To investigate the feasibility of a robotic system with artificial intelligence-based lesion detection and path planning for computed tomography (CT)-guided biopsy compared with the conventional freehand technique.
CXR-LLaVA: a multimodal large language model for interpreting chest X-ray images.
European radiology
Jan 15, 2025
OBJECTIVE: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation...
Evaluation of a Deep Learning Denoising Algorithm for Dose Reduction in Whole-Body Photon-Counting CT Imaging: A Cadaveric Study.
Academic radiology
Jan 15, 2025
RATIONALE AND OBJECTIVES: Photon Counting CT (PCCT) offers advanced imaging capabilities with potential for substantial radiation dose reduction; however, achieving this without compromising image quality remains a challenge due to increased noise at...
Evaluating a clinically available artificial intelligence model for intracranial aneurysm detection: a multi-reader study and algorithmic audit.
Neuroradiology
Jan 15, 2025
PURPOSE: We aimed to validate a clinically available artificial intelligence (AI) model to assist general radiologists in the detection of intracranial aneurysm (IA) in a multi-reader multi-case (MRMC) study, and to explore its performance in routine...
Attention-guided erasing for enhanced transfer learning in breast abnormality classification.
International journal of computer assisted radiology and surgery
Jan 15, 2025
PURPOSE: Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, att...
A novel hybrid deep learning framework based on biplanar X-ray radiography images for bone density prediction and classification.
Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
Jan 15, 2025
UNLABELLED: This study utilized deep learning for bone mineral density (BMD) prediction and classification using biplanar X-ray radiography (BPX) images from Huashan Hospital Medical Checkup Center. Results showed high accuracy and strong correlation...
Generative Adversarial Networks With Radiomics Supervision for Lung Lesion Generation.
IEEE transactions on bio-medical engineering
Jan 15, 2025
Data-driven methods for lesion generation are quickly emerging due to the need for realistic imaging targets for image quality assessment and virtual clinical trials. We proposed a generative adversarial network (GAN) architecture for conditional gen...
A novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs.
Oral radiology
Jan 14, 2025
OBJECTIVES: Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture.
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